Overview

Brought to you by YData

Dataset statistics

Number of variables8
Number of observations52416
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.6 MiB
Average record size in memory112.3 B

Variable types

Numeric8

Alerts

DiffuseFlows is highly overall correlated with GeneralDiffuseFlowsHigh correlation
GeneralDiffuseFlows is highly overall correlated with DiffuseFlowsHigh correlation
PowerConsumption_Zone1 is highly overall correlated with PowerConsumption_Zone2 and 1 other fieldsHigh correlation
PowerConsumption_Zone2 is highly overall correlated with PowerConsumption_Zone1 and 1 other fieldsHigh correlation
PowerConsumption_Zone3 is highly overall correlated with PowerConsumption_Zone1 and 1 other fieldsHigh correlation

Reproduction

Analysis started2024-07-31 13:38:59.721795
Analysis finished2024-07-31 13:39:08.281901
Duration8.56 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

Temperature
Real number (ℝ)

Distinct3437
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.810024
Minimum3.247
Maximum40.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-07-31T10:39:08.550685image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum3.247
5-th percentile9.8
Q114.41
median18.78
Q322.89
95-th percentile28.39
Maximum40.01
Range36.763
Interquartile range (IQR)8.48

Descriptive statistics

Standard deviation5.8154758
Coefficient of variation (CV)0.30916898
Kurtosis-0.30332122
Mean18.810024
Median Absolute Deviation (MAD)4.26
Skewness0.19671914
Sum985946.22
Variance33.819759
MonotonicityNot monotonic
2024-07-31T10:39:08.724839image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.18 58
 
0.1%
20.76 56
 
0.1%
19.79 55
 
0.1%
20.74 52
 
0.1%
20.83 51
 
0.1%
15.85 51
 
0.1%
15.84 51
 
0.1%
21 50
 
0.1%
20.89 50
 
0.1%
20.37 49
 
0.1%
Other values (3427) 51893
99.0%
ValueCountFrequency (%)
3.247 1
< 0.1%
3.441 1
< 0.1%
3.541 1
< 0.1%
3.555 1
< 0.1%
3.582 1
< 0.1%
3.629 1
< 0.1%
3.638 1
< 0.1%
3.662 1
< 0.1%
3.681 1
< 0.1%
3.706 1
< 0.1%
ValueCountFrequency (%)
40.01 1
< 0.1%
39.78 1
< 0.1%
39.76 1
< 0.1%
39.74 1
< 0.1%
39.73 1
< 0.1%
39.7 1
< 0.1%
39.67 1
< 0.1%
39.6 1
< 0.1%
39.59 1
< 0.1%
39.55 1
< 0.1%

Humidity
Real number (ℝ)

Distinct4443
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.259518
Minimum11.34
Maximum94.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-07-31T10:39:08.941466image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum11.34
5-th percentile39.45
Q158.31
median69.86
Q381.4
95-th percentile88.9
Maximum94.8
Range83.46
Interquartile range (IQR)23.09

Descriptive statistics

Standard deviation15.551177
Coefficient of variation (CV)0.2278243
Kurtosis-0.12185965
Mean68.259518
Median Absolute Deviation (MAD)11.54
Skewness-0.62516601
Sum3577890.9
Variance241.83911
MonotonicityNot monotonic
2024-07-31T10:39:09.107817image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85.9 197
 
0.4%
84.6 190
 
0.4%
85 189
 
0.4%
86.6 187
 
0.4%
86.3 186
 
0.4%
85.8 185
 
0.4%
87.2 175
 
0.3%
86.8 173
 
0.3%
87.4 171
 
0.3%
86.9 171
 
0.3%
Other values (4433) 50592
96.5%
ValueCountFrequency (%)
11.34 2
< 0.1%
11.57 1
< 0.1%
11.94 1
< 0.1%
12.27 1
< 0.1%
12.3 1
< 0.1%
12.6 1
< 0.1%
12.74 1
< 0.1%
12.87 1
< 0.1%
13.04 1
< 0.1%
13.07 1
< 0.1%
ValueCountFrequency (%)
94.8 3
 
< 0.1%
94.7 4
 
< 0.1%
94.6 1
 
< 0.1%
94.5 2
 
< 0.1%
94.4 1
 
< 0.1%
94.3 2
 
< 0.1%
94.2 4
 
< 0.1%
94.1 6
< 0.1%
94 9
< 0.1%
93.9 10
< 0.1%

WindSpeed
Real number (ℝ)

Distinct548
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9594889
Minimum0.05
Maximum6.483
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-07-31T10:39:09.251870image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0.05
5-th percentile0.069
Q10.078
median0.086
Q34.915
95-th percentile4.923
Maximum6.483
Range6.433
Interquartile range (IQR)4.837

Descriptive statistics

Standard deviation2.348862
Coefficient of variation (CV)1.1987116
Kurtosis-1.7831692
Mean1.9594889
Median Absolute Deviation (MAD)0.016
Skewness0.46242332
Sum102708.57
Variance5.5171525
MonotonicityNot monotonic
2024-07-31T10:39:09.407922image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.082 2291
 
4.4%
0.083 1979
 
3.8%
0.084 1831
 
3.5%
0.081 1804
 
3.4%
0.085 1513
 
2.9%
0.08 1502
 
2.9%
0.073 1485
 
2.8%
4.919 1430
 
2.7%
4.916 1375
 
2.6%
0.072 1369
 
2.6%
Other values (538) 35837
68.4%
ValueCountFrequency (%)
0.05 1
 
< 0.1%
0.053 5
 
< 0.1%
0.054 10
< 0.1%
0.055 13
< 0.1%
0.056 9
< 0.1%
0.057 17
< 0.1%
0.058 4
 
< 0.1%
0.059 7
< 0.1%
0.06 9
< 0.1%
0.061 11
< 0.1%
ValueCountFrequency (%)
6.483 1
< 0.1%
6.325 1
< 0.1%
6.2 1
< 0.1%
5.817 1
< 0.1%
5.69 1
< 0.1%
5.402 1
< 0.1%
5.375 1
< 0.1%
5.044 1
< 0.1%
5.019 1
< 0.1%
5.014 2
< 0.1%

GeneralDiffuseFlows
Real number (ℝ)

HIGH CORRELATION 

Distinct10504
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean182.69661
Minimum0.004
Maximum1163
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-07-31T10:39:09.558055image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0.004
5-th percentile0.037
Q10.062
median5.0355
Q3319.6
95-th percentile782
Maximum1163
Range1162.996
Interquartile range (IQR)319.538

Descriptive statistics

Standard deviation264.40096
Coefficient of variation (CV)1.4472132
Kurtosis0.40276752
Mean182.69661
Median Absolute Deviation (MAD)5.0095
Skewness1.3069729
Sum9576225.7
Variance69907.867
MonotonicityNot monotonic
2024-07-31T10:39:09.709482image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.055 1576
 
3.0%
0.062 1557
 
3.0%
0.051 1497
 
2.9%
0.059 1474
 
2.8%
0.066 1459
 
2.8%
0.048 1388
 
2.6%
0.044 1292
 
2.5%
0.073 1262
 
2.4%
0.04 1125
 
2.1%
0.077 1116
 
2.1%
Other values (10494) 38670
73.8%
ValueCountFrequency (%)
0.004 3
 
< 0.1%
0.007 9
 
< 0.1%
0.011 38
 
0.1%
0.015 97
 
0.2%
0.018 184
 
0.4%
0.022 309
 
0.6%
0.026 436
0.8%
0.029 566
1.1%
0.033 699
1.3%
0.037 924
1.8%
ValueCountFrequency (%)
1163 1
< 0.1%
1122 1
< 0.1%
1102 1
< 0.1%
1099 1
< 0.1%
1082 1
< 0.1%
1069 1
< 0.1%
1055 1
< 0.1%
1051 1
< 0.1%
1050 1
< 0.1%
1044 1
< 0.1%

DiffuseFlows
Real number (ℝ)

HIGH CORRELATION 

Distinct10449
Distinct (%)19.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.028022
Minimum0.011
Maximum936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-07-31T10:39:09.894582image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0.011
5-th percentile0.085
Q10.122
median4.456
Q3101
95-th percentile331.85
Maximum936
Range935.989
Interquartile range (IQR)100.878

Descriptive statistics

Standard deviation124.21095
Coefficient of variation (CV)1.6555274
Kurtosis7.0029015
Mean75.028022
Median Absolute Deviation (MAD)4.393
Skewness2.4569065
Sum3932668.8
Variance15428.36
MonotonicityNot monotonic
2024-07-31T10:39:10.039956image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.115 1260
 
2.4%
0.122 1218
 
2.3%
0.119 1201
 
2.3%
0.126 1150
 
2.2%
0.111 1140
 
2.2%
0.13 1093
 
2.1%
0.104 1085
 
2.1%
0.1 963
 
1.8%
0.137 926
 
1.8%
0.096 915
 
1.7%
Other values (10439) 41465
79.1%
ValueCountFrequency (%)
0.011 1
 
< 0.1%
0.019 3
 
< 0.1%
0.022 2
 
< 0.1%
0.026 4
 
< 0.1%
0.03 10
 
< 0.1%
0.033 18
 
< 0.1%
0.037 20
 
< 0.1%
0.041 34
0.1%
0.044 53
0.1%
0.048 74
0.1%
ValueCountFrequency (%)
936 1
< 0.1%
933 1
< 0.1%
922 2
< 0.1%
909 1
< 0.1%
903 1
< 0.1%
897 1
< 0.1%
863 1
< 0.1%
856 1
< 0.1%
855 1
< 0.1%
851 1
< 0.1%

PowerConsumption_Zone1
Real number (ℝ)

HIGH CORRELATION 

Distinct27709
Distinct (%)52.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32344.971
Minimum13895.696
Maximum52204.395
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-07-31T10:39:10.175187image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum13895.696
5-th percentile21867.342
Q126310.669
median32265.92
Q337309.018
95-th percentile44712.058
Maximum52204.395
Range38308.699
Interquartile range (IQR)10998.349

Descriptive statistics

Standard deviation7130.5626
Coefficient of variation (CV)0.22045352
Kurtosis-0.75405439
Mean32344.971
Median Absolute Deviation (MAD)5513.8039
Skewness0.22886369
Sum1.695394 × 109
Variance50844922
MonotonicityNot monotonic
2024-07-31T10:39:10.322428image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34560 30
 
0.1%
23040 24
 
< 0.1%
28800 19
 
< 0.1%
25920 18
 
< 0.1%
23672.42196 13
 
< 0.1%
35441.31148 13
 
< 0.1%
21063.34426 12
 
< 0.1%
21950.76923 12
 
< 0.1%
31680 12
 
< 0.1%
22880.68085 11
 
< 0.1%
Other values (27699) 52252
99.7%
ValueCountFrequency (%)
13895.6962 1
< 0.1%
13920 1
< 0.1%
13932.1519 1
< 0.1%
14090.12658 1
< 0.1%
14327.08861 1
< 0.1%
14557.97468 1
< 0.1%
14612.65823 1
< 0.1%
15013.67089 1
< 0.1%
15524.05063 1
< 0.1%
15572.65823 1
< 0.1%
ValueCountFrequency (%)
52204.39512 1
< 0.1%
52146.85905 1
< 0.1%
52038.1798 1
< 0.1%
51955.07214 1
< 0.1%
51916.71476 1
< 0.1%
51820.82131 1
< 0.1%
51776.07103 1
< 0.1%
51737.71365 1
< 0.1%
51731.32075 1
< 0.1%
51718.53496 1
< 0.1%

PowerConsumption_Zone2
Real number (ℝ)

HIGH CORRELATION 

Distinct29621
Distinct (%)56.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21042.509
Minimum8560.0815
Maximum37408.861
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-07-31T10:39:10.507574image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum8560.0815
5-th percentile13284.146
Q116980.766
median20823.168
Q324713.718
95-th percentile30387.137
Maximum37408.861
Range28848.779
Interquartile range (IQR)7732.9515

Descriptive statistics

Standard deviation5201.4659
Coefficient of variation (CV)0.24718848
Kurtosis-0.43739724
Mean21042.509
Median Absolute Deviation (MAD)3867.0469
Skewness0.32887602
Sum1.1029642 × 109
Variance27055247
MonotonicityNot monotonic
2024-07-31T10:39:10.664647image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21600 16
 
< 0.1%
25200 12
 
< 0.1%
14148.32827 11
 
< 0.1%
23400 11
 
< 0.1%
22800 11
 
< 0.1%
16158.83576 11
 
< 0.1%
13732.5228 10
 
< 0.1%
18000 10
 
< 0.1%
13539.20973 10
 
< 0.1%
22962.16216 10
 
< 0.1%
Other values (29611) 52304
99.8%
ValueCountFrequency (%)
8560.081466 1
< 0.1%
8585.743381 1
< 0.1%
8633.401222 1
< 0.1%
8651.731161 1
< 0.1%
8787.372709 1
< 0.1%
8897.352342 1
< 0.1%
8912.016293 1
< 0.1%
9131.97556 1
< 0.1%
9307.942974 1
< 0.1%
9365.944272 1
< 0.1%
ValueCountFrequency (%)
37408.86076 1
< 0.1%
36645.56962 1
< 0.1%
36482.78775 1
< 0.1%
36437.17001 1
< 0.1%
36429.56705 1
< 0.1%
36391.55227 1
< 0.1%
36353.53749 1
< 0.1%
36201.47835 1
< 0.1%
36129.25026 1
< 0.1%
36110.24287 1
< 0.1%

PowerConsumption_Zone3
Real number (ℝ)

HIGH CORRELATION 

Distinct22838
Distinct (%)43.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17835.406
Minimum5935.1741
Maximum47598.326
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-07-31T10:39:10.812797image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum5935.1741
5-th percentile9519.3277
Q113129.327
median16415.117
Q321624.1
95-th percentile29905.709
Maximum47598.326
Range41663.152
Interquartile range (IQR)8494.7738

Descriptive statistics

Standard deviation6622.1651
Coefficient of variation (CV)0.3712932
Kurtosis1.0863933
Mean17835.406
Median Absolute Deviation (MAD)3866.1593
Skewness1.0238715
Sum9.3486065 × 108
Variance43853071
MonotonicityNot monotonic
2024-07-31T10:39:10.946611image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17280 26
 
< 0.1%
11520 19
 
< 0.1%
9600 17
 
< 0.1%
9450.180072 17
 
< 0.1%
9588.47539 16
 
< 0.1%
9173.589436 15
 
< 0.1%
9795.918367 15
 
< 0.1%
16366.26506 15
 
< 0.1%
9617.286915 15
 
< 0.1%
9329.171669 14
 
< 0.1%
Other values (22828) 52247
99.7%
ValueCountFrequency (%)
5935.17407 1
< 0.1%
6044.657863 1
< 0.1%
6061.944778 1
< 0.1%
6108.043217 1
< 0.1%
6119.567827 1
< 0.1%
6182.953181 1
< 0.1%
6200.240096 1
< 0.1%
6211.764706 1
< 0.1%
6223.289316 1
< 0.1%
6252.10084 1
< 0.1%
ValueCountFrequency (%)
47598.32636 2
< 0.1%
47580.25105 1
< 0.1%
47507.94979 1
< 0.1%
47441.67364 1
< 0.1%
47435.64854 1
< 0.1%
47429.62343 1
< 0.1%
47405.52301 1
< 0.1%
47291.04603 1
< 0.1%
47278.99582 1
< 0.1%
47266.94561 1
< 0.1%

Interactions

2024-07-31T10:39:06.892333image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:00.520710image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:01.390862image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:02.238806image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:03.117096image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:03.932763image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:04.824019image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:05.728820image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:07.001321image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:00.637185image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:01.489793image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:02.347312image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:03.223192image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:04.039074image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:04.950138image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:05.840380image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:07.089692image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:00.740035image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:01.592979image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:02.443770image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:03.322691image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:04.130232image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:05.072906image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:06.190847image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:07.191538image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:00.852023image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:01.716890image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:02.548741image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:03.423697image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:04.241524image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:05.183864image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:06.291547image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:07.307733image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:00.960943image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:01.816889image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:02.655802image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:03.525687image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:04.383474image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:05.295677image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:06.399257image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:07.408708image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:01.064525image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:01.903967image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:02.754555image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:03.621417image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:04.472000image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:05.403333image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:06.502344image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:07.540387image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:01.173152image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:02.046343image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:02.923468image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:03.733018image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:04.574988image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:05.522580image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:06.620352image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:07.661072image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:01.287861image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:02.143981image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:03.024177image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:03.833730image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:04.718097image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:05.627766image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-07-31T10:39:06.772777image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Correlations

2024-07-31T10:39:11.038537image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
DiffuseFlowsGeneralDiffuseFlowsHumidityPowerConsumption_Zone1PowerConsumption_Zone2PowerConsumption_Zone3TemperatureWindSpeed
DiffuseFlows1.0000.786-0.2530.1250.097-0.0210.2610.022
GeneralDiffuseFlows0.7861.000-0.4590.2610.2420.0850.4720.157
Humidity-0.253-0.4591.000-0.300-0.309-0.212-0.378-0.181
PowerConsumption_Zone10.1250.261-0.3001.0000.8510.7480.4330.107
PowerConsumption_Zone20.0970.242-0.3090.8511.0000.5380.3790.087
PowerConsumption_Zone3-0.0210.085-0.2120.7480.5381.0000.4360.079
Temperature0.2610.472-0.3780.4330.3790.4361.0000.326
WindSpeed0.0220.157-0.1810.1070.0870.0790.3261.000

Missing values

2024-07-31T10:39:07.797147image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-31T10:39:08.037813image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

TemperatureHumidityWindSpeedGeneralDiffuseFlowsDiffuseFlowsPowerConsumption_Zone1PowerConsumption_Zone2PowerConsumption_Zone3
Datetime
1/1/2017 0:006.55973.80.0830.0510.11934055.6962016128.8753820240.96386
1/1/2017 0:106.41474.50.0830.0700.08529814.6835419375.0759920131.08434
1/1/2017 0:206.31374.50.0800.0620.10029128.1012719006.6869319668.43373
1/1/2017 0:306.12175.00.0830.0910.09628228.8607618361.0942218899.27711
1/1/2017 0:405.92175.70.0810.0480.08527335.6962017872.3404318442.40964
1/1/2017 0:505.85376.90.0810.0590.10826624.8101317416.4133718130.12048
1/1/2017 1:005.64177.70.0800.0480.09625998.9873416993.3130717945.06024
1/1/2017 1:105.49678.20.0850.0550.09325446.0759516661.3981817459.27711
1/1/2017 1:205.67878.10.0810.0660.14124777.7215216227.3556217025.54217
1/1/2017 1:305.49177.30.0820.0620.11124279.4936715939.2097316794.21687
TemperatureHumidityWindSpeedGeneralDiffuseFlowsDiffuseFlowsPowerConsumption_Zone1PowerConsumption_Zone2PowerConsumption_Zone3
Datetime
12/30/2017 22:207.65070.10.0810.0620.12234323.9543728676.2810715684.99400
12/30/2017 22:307.48071.00.0850.0620.10433776.4258628230.7456315546.69868
12/30/2017 22:407.39071.20.0790.0660.10033387.0722427814.6670815396.87875
12/30/2017 22:507.34071.00.0840.0370.11932815.2091327564.2835215172.14886
12/30/2017 23:007.07072.50.0800.0590.09332158.1749027273.3967514987.75510
12/30/2017 23:107.01072.40.0800.0400.09631160.4562726857.3182014780.31212
12/30/2017 23:206.94772.60.0820.0510.09330430.4182526124.5780914428.81152
12/30/2017 23:306.90072.80.0860.0840.07429590.8745225277.6925413806.48259
12/30/2017 23:406.75873.00.0800.0660.08928958.1749024692.2368813512.60504
12/30/2017 23:506.58074.10.0810.0620.11128349.8098924055.2316713345.49820